Mapping Energy Access and Solar Potential
UNDP
Nigeria Data Hub
Nigeria at Night
VIIRS Daily Composite Averages
(2022–2024)
Access to reliable electricity remains a significant challenge across sub-Saharan Africa. Nigeria, Africa’s most populous and the continent’s second largest economy, continues to grapple with widespread energy poverty, impacting households, productive sectors, and essential public service delivery.
To support more targeted and evidence-based energy planning, this analysis leverages satellite-derived nighttime lights data as a spatial proxy for electricity access. Specifically, daily observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor, using the Gap-Filled DNB BRDF-Corrected Nighttime Lights product at 500-meter resolution, was used to analyze lighting patterns across Nigeria from 2022 to 2024. Daily VIIRS nightlights data from 2022 to 2024 (Gap_Filled_DNB_BRDF_Corrected_NTL band) were composited into annual mean rasters covering the period 2020-2024.
By analyzing the intensity and spatial distribution of nighlights across Nigeria, we estimate the electrification footprint and highlight persistently dark or under-served areas that may lack grid connectivity. While the methodology is optimised for in-country analysis, the results are not intended for cross-country comparisons as normalisation was performed using Nigeria specific distributions.
This analysis provides a foundation for more granular energy access assessments and helps identify high-priority locations for solar and decentralized energy interventions, particularly in rural and peri-urban areas with limited infrastructure.
Energy Access Clusters
Gaussian Mixture Model
(GMM) Classification
To estimate spatial patterns of electricity access across Nigeria, satellite-derived nighttime light intensity data (VIIRS, 500m resolution, 2022–2024) were normalized, transformed and analysed using unsupervised clustering techniques to improve comparability across geographic areas. Several transformation techniques were tested, including logarithmic scaling, square root, Box-Cox, and histogram equalization, to optomise the distribution of light values. Ultimately, the normalized mean raster was selected for clustering, as it exhibited a near-symmetric, bell-shaped distribution ideal for unsupervised classification.
Two clustering algorithms were evaluated: K-Means and Gaussian Mixture Modeling (GMM). Both methods produced broadly consistent results, with over 80% spatial overlap in cluster assignments. However, GMM was selected for the final classification due to its probabilistic structure, which better captures gradual transitions and ambiguity, particularly in peri-urban or semi-electrified areas where light intensity changes are not abrupt.
Unlike K-Means, which imposes hard boundaries between clusters, GMM models overlapping distributions and is therefore more flexible in representing mixed or transitional access zones. Silhouette analysis supported a four-cluster solution, offering the best trade-off between statistical cohesion and interpretability.
The final classification assigned each 500m pixel to one of four electricity access tiers based on the distribution of normalized light intensity values:
No/Very Low Access: ≤ 0.612
Low Access: 0.612 – 0.730
Medium Access: 0.730 – 0.890
High Access: > 0.890
This GMM-based typology provides a spatially continuous, high-resolution map of electricity access across Nigeria. It enables a more targeted planning and identification of under-served populations, including hidden deprivation within partially electrified regions.
Energy Access Typologies
Cluster-Specific
Spatial Patterns
The four maps depict distinct typologies of energy access derived from the GMM classification. Each typology corresponds to a different access tier and reveals the spatial distribution of energy inequality across Nigeria:
Dark purple areas indicate regions with no or minimal electricity access. These zones are predominantly located across the northern belt and represent the most energy-deprived parts of the country.
Blue areas represent zones with limited but existing access, often forming transitional buffers between poorly electrified regions and areas with moderate access. These tend to cluster around rural-urban interfaces or semi-electrified zones.
Green areas correspond to moderate electricity access, typically forming a halo around major cities and dense settlements. They are most prominent in the middle belt and northern fringes of southern Nigeria.
Yellow areas denote high electricity access, concentrated around major urban hubs such as Lagos, Abuja, and Port Hartcourt, as well as in the oil-producing Niger Delta. These areas also appear around isolated city centers in the north, highlighting localized electrification.
Together, the maps provide a nuanced spatial typology of electricity access, enabling visual identification of priority regions for energy investment and solar deployment.
Population Density and Energy Access Identifying Underserved High-Density Areas
To pinpoint where energy deprivation intersects most critically with human settlement, the analysis overlays the GMM classified electricity access typologies with smoothed population density surfaces derived from the Global Human Settlement Layer (GHS-POP). GHS-POP provides gridded population estimates at ~1km resolution, enabling disaggregated spatial analysis across the four electricity access tiers.
To emphasize regional clusters over isolated outliers, a 51×51 pixel kernel smoothing filter was applied to the population rasters within each access category. This revealed not only where people live, but how densely they are concentrated. Pixels with fewer than 100 people/km² were rendered in gray to de-emphasize sparsely populated zones and instead highlight areas with concentrated human presence.
The resulting maps reveal a stark spatial mismatch in many parts of the country: large, densely populated areas, particularly across northern Nigeria and the Middle Belt, fall within zones classified as having very low or no electricity access. These hotspots represent high-priority intervention zones, where infrastructure deficits directly affect large populations.
Conversely, high-access zones align closely with major urban centers particularly in the southern regions of the country, confirming an urban–infrastructure nexus. Medium access areas form a transitional ring around secondary cities and expanding peri-urban zones, while low access areas show scattered yet significant population densities, often in emerging rural towns or underserved settlements.
Some areas within the “very low access” class appear sparsely populated. This is expected in locations such as national parks, wetlands, or terrain unsuitable for habitation, and may also reflect limitations of satellite-based population detection in dispersed rural areas.
By intersecting electricity access classifications with population density, the analysis shifts from infrastructure mapping to human-centered energy diagnostics, providing a sharper basis for prioritizing interventions based on real-world needs.